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model_stockAXCXEPT/Qwen3-EZO-8B-betabfloat16AXCXEPT/Qwen3-EZO-8B-beta1name: ZeroXClem-Qwen3-8B-HoneyBadger-EXP
2base_model: AXCXEPT/Qwen3-EZO-8B-beta
3dtype: bfloat16
4merge_method: model_stock
5models:
6 - model: taki555/Qwen3-8B-Shadow-FT-BAAI-2k
7 - model: GreenerPastures/Bald-Beaver-8B
8 - model: YOYO-AI/Qwen3-8B-YOYO
9 - model: KaraKaraWitch/CavesOfQwen3-8b
10tokenizer_source: AXCXEPT/Qwen3-EZO-8B-beta| Model | Highlights |
|---|---|
KaraKaraWitch/CavesOfQwen3-8b | Loosens baked-in instruct bias for more natural RP and abstract depth |
YOYO-AI/Qwen3-8B-YOYO | Della-style merge optimized for rich conversational alignment |
AXCXEPT/Qwen3-EZO-8B-beta | MT-Bench 9.08, deep-thought prompting, vLLM friendly |
GreenerPastures/Bald-Beaver-8B | Uncensored storytelling and immersive character dialogue |
taki555/Qwen3-8B-Shadow-FT-BAAI-2k | Shadow-FT tuned for precise instruction-following on BAAI-2k |
FROM https://hf.co/ZeroXClem/Qwen3-8B-HoneyBadger-EXP-Q4_K_M-GGUF:latest
PARAMETER temperature 0.6
PARAMETER top_p 0.95
PARAMETER repeat_penalty 1.05
PARAMETER top_k 20
TEMPLATE """"{{- if .Messages }}
{{- if or .System .Tools }}<|im_start|>system
{{- if .System }}
{{ .System }}
{{- end }}
{{- if .Tools }}
# Tools
You may call one or more functions to assist with the user query.
You are provided with function signatures within <tools></tools> XML tags:
<tools>
{{- range .Tools }}
{"type": "function", "function": {{ .Function }}}
{{- end }}
</tools>
For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>
{{- end }}<|im_end|>
{{ end }}
{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 -}}
{{- if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
{{ else if eq .Role "assistant" }}<|im_start|>assistant
{{ if .Content }}{{ .Content }}
{{- else if .ToolCalls }}<tool_call>
{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
{{ end }}</tool_call>
{{- end }}{{ if not $last }}<|im_end|>
{{ end }}
{{- else if eq .Role "tool" }}<|im_start|>user
<tool_response>
{{ .Content }}
</tool_response><|im_end|>
{{ end }}
{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
{{ end }}
{{- end }}
{{- else }}
{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
SYSTEM """# System Prompt: Universal Coder and DevOps Expert
You are an advanced AI assistant specializing in coding and DevOps. Your role is to provide expert guidance, code solutions, and best practices across a wide range of programming languages, frameworks, and DevOps tools. Your knowledge spans from low-level systems programming to high-level web development, cloud infrastructure, and everything in between.
## Key responsibilities:
1. Code analysis and optimization
2. Debugging and troubleshooting
3. Architecture design and system planning
4. Version Control best practices (Git)
5. Building from source, extracting binaries, and building packages & executeables including bash scripts.
6. Security and implementation and auditing
7. Performance review, and code analysis with practical suggestions in fully functioning syntax.
Be VERY selective on choosing how to respond based on the user query. If the above responsibilities don't apply then respond to the best of your ability with the given context to COMPLETELY satisfy the user query.
### Guidance
When assisting users:
- Provide clear, concise, and well-commented code examples
- Explain complrex concepts in simple terms
- Offer multiple solutions when applicable, highlighting pros and cons
- Prioritize security, efficiency, scalability, and maintainability in all suggestions
- Adapt your communication style for expert users.
### Helpful
Be EXTREMELY helpful, insightful, and lucid."""ollama run hf.co/ZeroXClem/Qwen3-8B-HoneyBadger-EXP-Q4_K_M-GGUF1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"
4
5tokenizer = AutoTokenizer.from_pretrained(model_name)
6model = AutoModelForCausalLM.from_pretrained(
7 model_name,
8 torch_dtype="auto",
9 device_map="auto"
10)
11
12prompt = "Write a short story about a detective solving a paradox in time."
13
14inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
15outputs = model.generate(**inputs, max_new_tokens=300)
16print(tokenizer.decode(outputs[0], skip_special_tokens=True))